New statistical framework for interlaboratory evaluation of anti-doping testing results by WADA
Bibliographic record
Abstract
Abstract The World Anti-doping Agency (WADA) International Standard for Laboratories (ISL), developed as part of the World Anti-Doping Program, requires satisfactory laboratory performance in the WADA External Quality Assessment Scheme (EQAS) in order to obtain and maintain WADA accreditation. Under this mandate, WADA regularly distributes urine and blood test samples to anti-doping laboratories to continuously monitor their proficiency. Over the years, WADA has employed classical, generic statistical methods, in accordance to ISO 13528, to evaluate quantitative EQAS results. Here, we set out the rationale for a modern statistical approach that recognizes and addresses the particular features of the measurement results typically obtained in such tests and present an approach involving Bayesian measurement models and statistical data analysis that is tailored specifically to anti-doping testing.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.095 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".